Medical data processing apparatus, medical data processing method, and medical image diagnostic apparatus

ABSTRACT

A medical data processing apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a compressed channel of data generated by compressing a plurality of first medical channels of data defined by first domain representation and respectively corresponding to a plurality of components, via an intermediate channel of data defined by second domain representation. The processing circuitry decodes the compressed channel of data to a second medical channel of data defined by the first domain representation based on a conversion process from the plurality of first medical channels of data to the compressed dataset.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromthe Japanese Patent Application No. 2019-152273, filed Aug. 22, 2019,the entire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a medical dataprocessing apparatus, a medical data processing method, and a medicalimage diagnostic apparatus.

BACKGROUND

Accompanying an increase in an amount of medical data, compression anddecoding are being performed on medical data. Multicomponent medical rawdata is obtained through data acquisition with the use of dual-energyscanning in X-ray computed tomography (CT) or a plurality of receiverchannels in magnetic resonance imaging (MRI). Since there is a largeamount of such multicomponent medical raw data, more efficientcompression and decoding are desired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing a configuration of a medical data processingapparatus according to a present embodiment.

FIG. 2 is a diagram showing a typical flow of compression processingthat processing circuitry illustrated in FIG. 1 performs on a medicalraw dataset.

FIG. 3 is a schematic diagram showing base conversion, which isillustrated in FIG. 2.

FIG. 4 is a schematic diagram showing quantization, which is illustratedin FIG. 2.

FIG. 5 is a diagram showing an example of a zigzag scan in entropycoding, which is illustrated in FIG. 2.

FIG. 6 is a schematic diagram showing a relationship between input andoutput of a trained model for compression.

FIG. 7 is a diagram showing a typical flow of compression processingthat processing circuitry 11 performs on medical raw datasets when atrained model for compression is generated and stored for each imagingbody part.

FIG. 8 is a diagram showing a correspondence relationship between groupsof X-ray detector channels and quantization tables.

FIG. 9 is a diagram showing a relationship between input and output of atrained model for compression, which differs in number of componentsbetween input and output.

FIG. 10 is a diagram showing a typical flow of decoding processing thatthe processing circuitry illustrated in FIG. 1 performs on a compresseddataset.

FIG. 11 is a schematic diagram showing a relationship between input andoutput of a trained model for decoding.

FIG. 12 is a diagram showing a typical flow of decoding processing thatprocessing circuitry performs on a compressed dataset when a trainedmodel for decoding is generated and stored for each imaging body part.

FIG. 13 is a diagram showing a relationship between input and output ofa trained model for decoding that differs in number of componentsbetween input and output.

FIG. 14 is a diagram showing a configuration of a medical imagediagnostic apparatus according to a first modification.

FIG. 15 is a diagram showing a configuration of a medical dataprocessing system according to a second modification.

DETAILED DESCRIPTION

In general, according to one embodiment, a medical data processingapparatus includes processing circuitry. The processing circuitryobtains a compressed dataset generated by compressing a plurality ofmedical first datasets defined by first domain representation andrespectively corresponding to a plurality of components, via anintermediate dataset defined by second domain representation. Theprocessing circuitry decodes the compressed dataset to a second medicaldataset defined by the first domain representation based on a conversionprocess from the plurality of first medical datasets to the compresseddataset.

Hereinafter, a medical data processing apparatus, medical dataprocessing method, and a medical image diagnostic apparatus according tothe present embodiment will be described with reference to theaccompanying drawings.

The medical data processing apparatus according to the presentembodiment corresponds to a computer configured to process medical data.Medical data corresponds to raw data (hereinafter referred to as medicalraw data) or image data (hereinafter referred to as medical image data)collected by the medical image diagnostic apparatus. The medical imagediagnostic apparatus may be a single-modality apparatus or acomposite-modality apparatus. Examples of the single-modality apparatusinclude an X-ray computed tomography apparatus (CT apparatus), amagnetic resonance imaging apparatus (MRI apparatus), an X-raydiagnostic apparatus, a positron emission tomography (PET) apparatus, asingle photon emission CT (SPECT) apparatus, and an ultrasonicdiagnostic apparatus. Examples of the composite-modality apparatusinclude a PET/CT apparatus, a SPECT/CT apparatus, a PET/MRI apparatus,and a SPECT/MRI apparatus. Alternatively, the medical image diagnosticapparatus may be an optical interference tomographic apparatus or anultrasonic diagnostic apparatus.

When the medical image diagnostic apparatus is a CT apparatus, a gantryof the CT apparatus applies X-rays to a subject from an X-ray tube whilerotating the X-ray tube and an X-ray detector around the subject, anddetects by the X-ray detector the X-rays passed through the subject. Inthe X-ray detector, an electric signal having a crest valuecorresponding to the detected X-ray dose is generated. This electricsignal is subjected to signal processing such as A/D conversion by dataacquisition circuitry. The A/D converted electrical signal is referredto as projection data or sinogram data. The projection data or sinogramdata corresponds to a type of medical raw data.

When the medical image diagnostic apparatus is an MRI apparatus, agantry of the MRI apparatus repeats application of the gradient magneticfield by way of a gradient magnetic field coil and application of RFpulses by way of a transmission coil under the application of the staticmagnetic field by way of a static magnetic field magnet. An MR signalfrom the subject is released in response to the application of the RFpulse. The released MR signal is received by way of a reception coil.The received MR signal is subjected to signal processing such as A/Dconversion by the reception circuitry. The A/D converted MR signal isreferred to as k-space data. The k-space data corresponds to a type ofmedical raw data.

When the medical image diagnostic apparatus is an ultrasonic probe ofthe ultrasonic diagnostic apparatus, the ultrasonic probe transmitsultrasonic beams from a plurality of ultrasonic vibrators into thesubject body, and receives the ultrasonic waves reflected from thesubject body by way of the ultrasonic vibrators. The ultrasonicvibrators generate an electric signal having a crest value correspondingto the sound pressure of the received ultrasonic waves. The electricsignal is subjected to the A/D conversion by the A/D converter providedin the ultrasonic probe or the like. The A/D converted electric signalis referred to as echo data. The echo data is a type of medical rawdata.

When the medical image diagnostic apparatus is a PET apparatus, a gantryof the PET apparatus simultaneously measures by simultaneous measurementcircuitry a pair of gamma rays with 511 keV, which are generated inaccordance with the annihilation of positrons generated fromradionuclides accumulated in the subject and electrons around theradionuclide, thereby generating digital data having digital valuesindicative of the energy value and detection position of the pair ofgamma rays. This digital data is referred to as coincidence data orsinogram data. The coincidence data or sinogram data is a type ofmedical raw data.

When the medical image diagnostic apparatus is an X-ray diagnosticapparatus, the irradiation is from the X-ray tube provided in the C-arm.The X-rays produced by the X-ray tube and transmitted through thesubject are received by an X-ray detector such as a flat panel display(FPD) arranged in the C-arm or arranged separately from the C-arm. TheX-ray detector generates an electric signal having a crest valuecorresponding to the detected X-ray dose, and performs signal processingsuch as A/D conversion on this electric signal. The A/D convertedelectrical signal is referred to as projection data or X-ray image data.The projection data or X-ray image data is a type of medical image data.

According to the present embodiment, medical raw data is not limited tooriginal raw data collected by the medical image diagnostic apparatus.For example, medical raw data may be computational medical raw data thatis generated by performing inverse conversion processing on medicalimage data. When medical raw data is collected by the CT apparatus, theinverse conversion processing corresponds to, e.g., forward projectionprocessing. When medical raw data is collected by the MRI apparatus, theinverse conversion processing corresponds to, e.g., Fouriertransformation processing.

FIG. 1 is a diagram showing a configuration of a medical data processingapparatus 1 according to a present embodiment. The medical dataprocessing apparatus 1 shown in FIG. 1 is a computer included in themedical image diagnostic apparatus or a computer separated from themedical image diagnostic apparatus.

As shown in FIG. 1, the medical data processing apparatus 1 includesprocessing circuitry 11, a communication interface 12, a display 13, aninput interface 14, and a memory circuitry 15.

The processing circuitry 11 includes a processor such as a CPU or GPU.By activating various programs installed in the memory circuitry 15,etc., the processor implements an obtaining function 111, a compressionfunction 112, a decoding function 113, an image generation function 114,a display control function 115, etc. The functions 111 to 115 arerespectively not limited to those realized by a single processingcircuitry. A plurality of independent processors may be combined intoprocessing circuitry, and each of the processors may execute theprograms to realize the functions 111 to 115. The processing circuitry11 may not necessarily implement all of the functions 111 to 115, andmay lack some of them. For example, the processing circuitry 11 may lackany one of the compression function 112, the image generation function114, and the display control function 115.

By implementing the obtaining function 111, the processing circuitry 11obtains medical data. For example, the processing circuitry 11 obtainsas medical data medical raw datasets acquired by the medical imagediagnostic apparatus. Medical raw datasets may be obtained by way of thecommunication interface 12, a portable storage medium, etc., or may beobtained from the memory circuitry 15 that stores medical raw datasetsreceived by way of the communication interface 12, a portable storagemedium, etc. The processing circuitry 11 may obtain a compressed datasetgenerated by the compression function 112. The processing circuitry 11may obtain a compressed dataset from other computers such as a medicalimage diagnostic apparatus, by way of the communication interface 12, aportable storage medium, etc., or may acquire a compressed datasetgenerated by the apparatus 1 itself and stored in the memory circuitry15.

More specifically, the processing circuitry 11 acquires multicomponentmedical raw data that is a compression target. In other words, theprocessing circuitry 11 obtains a plurality of medical raw datasetsrespectively corresponding to a plurality of components. Physically,components correspond to acquisition processes of respective medical rawdatasets that are compression targets. Medical raw datasets that arecompression targets form a group of medical raw datasets that aresubstantially the same in terms of position to be data-acquired and aredifferent in terms of acquisition process. Examples of a scan system foracquiring medical raw datasets respectively corresponding to componentsinclude dual energy scan, photon counting CT, and multi-channel datacollection.

Dual energy scan is performed by an X-ray computed tomography apparatusor an X-ray diagnostic apparatus. Dual energy scan is a scan system inwhich two types of tube voltage, a low tube voltage and a high tubevoltage, are alternatively switched. By dual energy scan, a projectiondataset corresponding to a low tube voltage and a projection datasetcorresponding to a high tube voltage are collected as medical rawdatasets respectively corresponding to components.

Photon counting CT is performed by an X-ray computed tomographyapparatus or an X-ray diagnostic apparatus. Photon counting CT is a scansystem in which the number of X-rays is counted for each energy bin byway of a photon counting type X-ray detector of the X-ray computedtomography apparatus or X-ray diagnostic apparatus. By photon countingCT, a plurality of projection datasets respectively corresponding to aplurality of energy bins are acquisitioned as a plurality of medical rawdatasets respectively corresponding to a plurality of components. Forexample, about 3 to 16 energy bins are provided.

Multi-channel data acquisition is performed by a magnetic resonanceimaging apparatus. Multi-channel data acquisition is a data acquisitionsystem in which k-space data is acquired by way of a plurality ofreceiver channels included in reception circuitry. By multi channel dataacquisition, a plurality of k-space datasets respectively correspondingto a plurality of receiver channels are acquired as a plurality ofmedical raw datasets respectively corresponding to a plurality ofcomponents. For example, in the case of an array coil, about 4 to 64receiver channels are mounted.

A medical per-channel raw dataset (hereinafter, simply referred to as a“medical raw dataset”) corresponding to one component is an aggregate ofmedical raw data that is a compression target, in particular, anaggregate of medical raw data that falls within a range of datanecessary for image reconstruction. For example, when medical raw datais projection data to be acquired by an X-ray computed tomographyapparatus, a medical raw dataset corresponding to one component includesprojection data of the number of views per rotation of a rotation frame.When medical raw data is k-space data to be acquired by a magneticresonance imaging apparatus, a medical raw dataset corresponding to onecomponent includes k-space data corresponding to the number and/or arange of acquisition lines necessary for filling up one k-space.

A medical raw dataset may be further divided. In the case of using anX-ray computed tomography apparatus, a medical raw dataset may beclassified as an aggregate of projection data corresponding to apredetermined number of rows instead of all rows in an X-ray detector.In the case of using a magnetic resonance imaging apparatus, a medicalraw dataset may be classified as an aggregate of k-space datacorresponding to blocks equal to the integral multiple of the number oftimes that read-out gradient magnetic field is applied, in other words,the number of echo trains, for each block of pulse sequences with blockssuch as fast field echo (FFE) or fast spin echo (FSE).

By implementing the compression function 112, the processing circuitry11 generates a compressed dataset obtained by compressing a plurality ofmedical raw datasets defined by a first domain representation andrespectively corresponding to a plurality of components, by way of anintermediate dataset defined by a second domain representation. Morespecifically, the processing circuitry 11 generates an intermediatedataset by performing base conversion on a plurality of medical rawdatasets respectively corresponding to a plurality of components,thereby generating a compressed dataset by performing quantization andentropy coding on the generated intermediate dataset. Conversion frommedical raw datasets respectively corresponding to components to anintermediate dataset is performed by means of base conversion.

By implementing the decoding function 113, the processing circuitry 11decodes the compressed dataset to a medical raw dataset (hereinafterreferred to as a decoded medical raw dataset) defined by the firstdomain representation, based on the conversion process from the medicalraw datasets to the compressed dataset. Specifically, the processingcircuitry 11 generates an intermediate dataset defined by the seconddomain representation by applying an entropy decoder and an inversequantization algorithm to a compressed dataset, thereby generating adecoded medical raw dataset by performing inverse base conversion on thegenerated intermediate dataset.

By implementing the image generation function 114, the processingcircuitry 11 generates medical image data based on a decoded medical rawdataset. More specifically, the processing circuitry 11 generatesmedical image data by performing reconstruction processing on a decodedmedical raw dataset. Examples of a reconstruction method include ananalytical image reconstruction method and an iterative approximationreconstruction method. Examples of the analytical image reconstructionmethod for CT image reconstruction include a filtered back projection(FBP) method, a convolution back projection (CBP) method, and theirapplications. Examples of the analytical image reconstruction method forMR image reconstruction include Fourier transformation, inverse Fouriertransformation, and their applications. Examples of the iterativeapproximation reconstruction method include an expectation maximization(EM) method, an algebraic reconstruction technique (ART) method, andtheir applications. Examples of the iterative approximationreconstruction method may include the above methods in combination withan analytical image reconstruction method such as FBP or Fouriertransformation, or the above methods incorporating noise reduction basedon a statistical model, a scanner model, an anatomical model, and/ormachine learning.

By implementing the display control function 115, the processingcircuitry 11 displays by way of the display 13 medical image datagenerated by the image generation function 114. In this implementation,the processing circuitry 11 may perform any image display processingsuch as gradation processing or scaling on medical image data. Whenmedical image data generated with the image generation function 114 isthree-dimensional image data, the processing circuitry 11 may convertthe medical image data into two-dimensional image data by performingthree-dimensional image processing thereon. As three-dimensional imageprocessing, the processing circuitry 11 may perform volume rendering,surface volume rendering, pixel value projection processing,multi-planer reconstruction (MPR) processing, curved MPR (CPR)processing, etc.

The communication interface 12 is an interface for data communicationwith the medical image diagnostic apparatus or with other computers.

The display 13 displays various kinds of information in accordance withthe display control function 115 of the processing circuitry 11. As thedisplay 13, a liquid crystal display (LCD), a cathode ray tube (CRT)display, an organic electro luminescence display (OELD), a plasmadisplay, or any other display may be suitably adopted. The display 13may be a projector.

The input interface 14 receives various input operations from a user,converts the received input operations into electric signals, andoutputs them to the processing circuitry 11. Specifically, the inputinterface 14 is coupled to input devices such as a mouse, a keyboard, atrack ball, a switch, buttons, a joystick, a touch pad and a touch paneldisplay. The input interface 14 outputs to the processing circuitry 11the electric signals corresponding to the input operations to the inputdevice. Furthermore, the input device connected to the input interface14 may be an input device provided in another computer connected via anetwork or the like.

The memory circuitry 15 is a storage device for storing various kinds ofinformation, such as a Read Only Memory (ROM), Random Access Memory(RAM), a Hard Disk Drive (HDD), a Solid State Drive (SSD), an integratedcircuit storage device, etc. The memory circuitry 15 stores, forexample, a medical raw dataset or a compressed dataset. Instead of theabove storage device, the memory circuitry 15 may be a driving devicefor reading and writing various kinds of information from and to aportable storage medium such as a compact disc (CD), a digital versatiledisc (DVD), a flash memory or the like, or a semiconductor memory devicesuch as a RAM. Alternatively, the memory circuitry 15 may be provided inanother computer connected to the medical data processing apparatus 1via a network.

Hereinafter, an operation example of the medical data processingapparatus 1 will be described.

FIG. 2 is a diagram showing a typical flow of compression processingthat the processing circuitry 11 performs on a medical raw dataset. Asshown in FIG. 2, with the obtaining function 111, the processingcircuitry 11 obtains a plurality of medical raw datasets D1 thatrespectively correspond to a plurality of components and are compressiontargets, in a step before the compression processing.

Upon acquisition of the medical raw datasets D1, the processingcircuitry 11 performs conversion processing on the medical raw datasetsD1 by implementing the compression function 112, thereby generating acompressed dataset D3. The processing circuitry 11 compresses themedical raw datasets D1 using both spatial and component correlations.More specifically, the processing circuitry 11 performs compression byreducing both redundancy of a spatial domain and redundancy betweencomponents included in the medical raw datasets D1.

Specifically, the processing circuitry 11 first generates a blenddataset D2 by performing base conversion on the medical raw datasets D1(step SA1).

FIG. 3 is a schematic diagram showing base conversion. In FIG. 3, themedical raw datasets are projection data collected by photon countingCT. As shown in FIG. 3, for example, a medical raw dataset correspondingto component C1, a medical raw dataset corresponding to component C2,and a medical raw dataset corresponding to component C3 are acquired.Each medical raw dataset is defined by two or more dimensional spacedomain representation. For example, as shown in FIG. 3, when a medicalraw dataset is defined by two-dimensional space domain representation,first axis x is defined in a row direction or a detector channeldirection of an X-ray detector, and second axis y is defined in a viewdirection. When a medical raw dataset is defined by three-dimensionalspace domain representation, a first axis is defined in a row directionof the X-ray detector, a second axis is defined in a detector channeldirection of the X-ray detector, and a third axis is defined in a viewdirection.

First, as shown in the middle of FIG. 3, the processing circuitry 11arranges a plurality of medical raw datasets respectively correspondingto a plurality of components in an information space defined by thefirst domain representation. The first domain representation includes aspace domain dimension and a component dimension. For example, in thecase of a two-dimensional medical raw dataset, first axis x of the firstdomain representation is defined in a row direction or a detectorchannel direction of the X-ray detector, second axis y is defined in aview direction, and third axis z is defined in a component direction. Inthe case of a three-dimensional medical raw dataset, a first axis of thefirst domain representation is defined in a row direction of theX-detector, a second axis is defined in a detector channel direction, athird axis is defined in a view direction, and a fourth axis is definedin a component direction. In this manner, the plurality of medical rawdatasets D1 are represented as three-dimensional or four-dimensionaldata.

Next, as shown at the bottom of FIG. 3, the processing circuitry 11generates the single blend dataset D2 defined by the second domainrepresentation by performing base conversion on the medical raw datasetsD1 arranged in the information space defined by the first domainrepresentation. The base conversion is processing for converting thefirst domain representation defined in a spatial domain direction and acomponent direction into the second domain representation defined in aspatial frequency direction. For example, orthogonal transform isperformed as the base conversion. As the orthogonal transform, forexample, any base conversion in an orthogonal relation, such as discretecosine transform (DCT), discrete sine transform, discrete Fouriertransform, discrete Hadamard transform, principle component analysis,etc., may be performed. In the following description, discrete cosinetransform is performed as the orthogonal transform.

In the discrete cosine transform, first, the processing circuitry 11divides the medical raw datasets D1 arranged in the information spaceinto a plurality of blocks having a predetermined matrix size.Typically, each block has the same dimensions as those of the firstdomain representation. For example, when the first domain representationis a three-dimensional representation composed of a two-dimensionalspatial domain and a one-dimensional component domain, a block has athree-dimensional configuration. In this case, an aggregate of themedical raw datasets D1 arranged in an information space is divided intothree-dimensional blocks. When the first domain representation is afour-dimensional representation composed of a three-dimensional spatialdomain and a one-dimensional component domain, a block has afour-dimensional configuration. In this case, an aggregate of themedical raw datasets D1 arranged in an information space is divided intofour-dimensional blocks.

The processing circuitry 11 applies discrete cosine transform to eachblock, thereby calculating a conversion coefficient. The processingcircuitry 11 generates the blend dataset D2 by arranging the calculatedconversion coefficient in the information space of the second domainrepresentation.

Conversion coefficient F (u, v, w) obtained by three-dimensionaldiscrete cosine transform is expressed by the following expression.Herein, f (j, k, l) represents a value of a medical raw dataset in point(j, k, l) in the information space. N represents a block size.

${F\left( {u,\nu,w} \right)} = {\frac{8{C(u)}{C(v)}{C(w)}}{N^{3}}{\sum\limits_{j = 0}^{N - 1}{\sum\limits_{k = 0}^{N - 1}{\sum\limits_{l = 0}^{N - 1}{{f\left( {j,k,l} \right)}\cos \; \frac{\left( {{2j} + 1} \right)u\; \pi}{2N}\cos \; \frac{\left( {{2k} + 1} \right)v\; \pi}{2N}\cos \; \frac{\left( {{2l} + 1} \right)w\; \pi}{2N}}}}}}$

Conversion coefficient F (u, v, w) is also referred to as a DCTcoefficient. DCT coefficient F (u, v, w) is represented with a floatingdecimal point, and each point of a blend dataset is assigned DCTcoefficient F (u, v, w). A conversion coefficient obtained byfour-dimensional discrete cosine transform is expandable in accordancewith the above expression.

The discrete cosine transform may be processed independently for eachdimension. For example, in the case of a three-dimensional informationspace, one-dimensional discrete cosine transform is applied to theplurality of the medical raw datasets D1 in the order of x-dimension,y-dimension, and z-dimension. This order of x-dimension, y-dimension,and z-dimension is not a limitation. The discrete cosine transform maybe performed in any order, for example, the order of z-dimension,y-dimension, and x-dimension. The same applies to the case offour-dimensional information space. Conversion coefficient F (u)obtained by one-dimensional discrete cosine transform can be expressedby the following expression.

${F(u)} = {\frac{2{C(u)}}{N}{\sum\limits_{j = 0}^{N - 1}{{f(j)}\cos \; \frac{\left( {{2j} + 1} \right)u\; \pi}{2N}}}}$

In the medical raw datasets D1, a low frequency component is dominant.Thus, the blend dataset D2 has a tendency in which a DCT coefficient ofa low frequency region has a relatively large value, while a DCTcoefficient of a high frequency region is nearly zero.

After step SA1, the processing circuitry 11 performs quantization on theblend dataset D2 using the quantization table T1 (step SA3).

FIG. 4 is a schematic diagram showing quantization. To simplify theillustration, the blend dataset D2 and the quantization table T1 in FIG.4 show numeric values for only the front surfaces. In each of the blenddataset D2 and the quantization table T1, the upper left directioncorresponds to a low frequency region and the lower right directioncorresponds to a high frequency region. Each point of the blend datasetD2 is assigned a DCT coefficient. The quantization table T1 has the sametable size as that of the blend dataset D2, and has each point assigneda table value previously determined in accordance with a preset rule.For example, a point in a low frequency region, which makes a relativelyhigh contribution to an image, is assigned a relatively small value. Incontrast, a point in a high frequency region, which makes a relativelylow contribution to an image, is assigned a relatively large value.

As shown in FIG. 4, the processing circuitry 11 generates a quantizeddataset by applying the quantization table T1 to the blend dataset D2.Specifically, the processing circuitry 11 divides a DCT coefficient ofthe blend dataset D2 having the same coordinates by a table value of thequantization table T1, thereby calculating a divided value. An integeris obtained from the divided value by, for example, rounding down afraction after the decimal point, rounding down a number less than 5 androunding up a number equal to or larger than 5, or rounding down anumber less than 5 and rounding up a number larger than 5. This integeris referred to as a quantization value. The integer processing realizesa reduced data length of quantization values at respective coordinates.Specifically in FIG. 4, see the point on the upper left end. When DCTcoefficient “298” is divided by table value “8”, divided value “37.25”is obtained. Then quantization value “37” is obtained by rounding downthe number after the decimal point, which is less than 5. The obtainedquantization value is assigned to a corresponding point in the quantizeddataset. The quantized dataset is generated by performing the aboveprocessing with respect to respective points.

After step SA2, the processing circuitry 11 performs entropy coding onthe quantized dataset using coding table T2 (step SA3). In this manner,compressed dataset D3 is generated. Specifically, the processingcircuitry 11 first scans a three-dimensional quantized dataset along apredetermined scanning order to rearrange the dataset in aone-dimensional progression (hereinafter referred to as a quantizationprogression), thereby performing entropy coding on the quantizationprogression. The scanning order is set to a predetermined order such asa zigzag scan or raster scan.

FIG. 5 shows an example of a zigzag scan. As shown in FIG. 5, thescanning order of a zigzag scan is determined in such a manner as tosample the quantized dataset from a low frequency region to a highfrequency region. The processing circuitry 11 specifies quantizationvalues of respective pixels in the quantized data in accordance with ascanning order of a zigzag scan, thereby rearranging the specifiedquantization values one-dimensionally from a starting point to an endingpoint of the scanning order. The zigzag scan provides a quantizationprogression in which quantization values are arranged in such a manneras to line up from the highest value to zero. In the quantized dataset,there is a tendency for regions other than the low frequency region tobe assigned zero values. Thus, quantization values not equal to zero arearranged in the section at the beginning of the progression, whereasquantization values equal to zero are arranged in the section after thebeginning. The scanning order of a zigzag scan shown in FIG. 5 is oneexample. The scanning order is not limited to this example and may be ascanning order for scanning strictly from a low frequency to a highfrequency.

Not only a standard scanning order but also a scanning order that isfreely determined may be adopted as a scanning order of a quantizationdataset. For example, the processing circuitry 11 specifies quantizationvalues of respective pixels of a given number, for example about 50, inquantized datasets, thereby determining such a scanning order that thequantization values become smaller from a starting point to an endingpoint. Specifically, a scanning order may be defined in accordance witha correlation between coordinates of a pixel and a sampling order ofthis particular pixel, and be registered in an LUT. The processingcircuitry 11 reads a scanning order from the LUT, thereby generating aquantization progression by scanning a quantized dataset in accordancewith the read scanning order.

There is no need to scan all the pixels in the quantized dataset. Whenall subsequent pixels exhibit a quantization value of zero, there is noneed to scan pixels subsequent to the last non-zero pixel.

When a quantization progression is generated, the processing circuitry11 generates compressed data by applying the quantization progression tothe coding table T2. As entropy coding, Huffman coding or arithmeticcompression coding is adopted. Specifically, for example, a variablelength code (VLC) or a context-dependent adaptive arithmetic compressionmethod defined by, e.g., H.264 or Joint Photographic Experts Group(JPEG) may be adopted. In the coding table T2, a content in accordancewith a variable length code or a context-dependent adaptive arithmeticcompression method is registered. In the case of using the variablelength code, a relation between quantization values and codes isregistered in the coding table T2. For example, quantization value“zero” that appears frequently is replaced with a code of a small numberof digits. A one-dimensional sequence of codes obtained in this manneris a compressed dataset. In the case of using the context-dependentadaptive arithmetic compression method, a context is set in such amanner as to inherit a value of a block physically adjacent to a blockthat is a processing target.

A compressed dataset may be correlated with attendant informationregarding a compression method. The attendant information is used todecode a medical raw dataset from a compressed dataset in decodingprocessing. Examples of the attendant information may include a type ofbase conversion, a type of quantization processing, a type of entropycoding, a quantization table, a coding table, etc. A compressed datasetmay be stored in the memory circuitry 15, or may be transferred by thecommunication interface 12, etc., to any other computer and storedtherein.

In this manner, the compression processing performed on a medical rawdataset by the processing circuitry 11 is completed.

According to the above compression processing, a plurality of medicalraw datasets that are defined by two or more dimensional domainrepresentations and respectively correspond to a plurality of componentscan be converted into a compressed dataset that is a one-dimensionalcode sequence. As described above, a plurality of medical raw datasetsrespectively corresponding to a plurality of components form a group ofdata pieces that are physically and substantially the same in terms ofimaging target and are different in terms of collection process, andeach component corresponds to a collection process. That is, there is atendency for various medical raw datasets that respectively correspondto various components to be highly similar to each other in terms ofspace distribution. In a compression process, the plurality of medicalraw datasets are collectively subjected to base conversion, therebybeing converted into a blend dataset. Thereafter, quantization andentropy coding are performed on the blend dataset. Not only a spatialdimension but also a component dimension is subjected to baseconversion. This enables highly efficient compression using not onlyredundancy of a spatial dimension but also redundancy of a componentdimension.

The above-described processing is based on the premise that medical rawdatasets respectively corresponding to all components collected by themedical image diagnostic apparatus are converted into a compresseddataset. However, the present embodiment is not limited to this. Inmedical raw datasets respectively corresponding to all componentscollected by the medical image diagnostic apparatus, for example,medical raw datasets corresponding to at least two or more componentsmay be converted. For example, steps SA1 to SA3 may be performed on onlya component that is a compression target selected by a user by way ofthe input interface 14 in a step before step SA1, so that only a medicalraw dataset corresponding to the component that is the compressiontarget may be converted into a compressed dataset.

The compression processing described above may adopt any method by whicha compressed dataset can be generated from a plurality of medical rawdatasets respectively corresponding to a plurality of components.Various modifications can made to the compression processing.Hereinafter, a modification of compression processing will be described.

Base conversion SA1 in the above compression processing may be performedthrough machine learning using a trained neural network (hereinafterreferred to as a trained model). In many cases, conversion obtainedthrough machine learning is non-linear conversion. However, in thisembodiment, non-liner conversion is also referred to as base conversionfor the sake of simplicity. A trained model includes a parameterizedsynthesis function defined by a combination of a plurality of adjustablefunctions and parameters (a weighting matrix or bias). A networkconfiguration of a trained model may be realized by a multi-layernetwork (Deep Neural Network: DNN) having an input layer, anintermediate layer, and an output layer. A trained model may be mountedas a program or may be physically mounted on a processor such as anASIC.

FIG. 6 is a schematic diagram showing a relationship between input andoutput of a trained model. As shown in FIG. 6, a trained modelcorresponds to a neural network that is trained in such a manner thatthe medical raw datasets D1 respectively corresponding to components areinput and a blend dataset D2 corresponding to the medical raw datasetsis output. As a type of a trained model, for example, a convolutionalneural network (CNN) is preferable.

A trained model may be generated by the medical data processingapparatus 1 or may be generated by any of other computers. Hereinafter,a computer having a processor such as a CPU, a GPU, etc., for generatinga trained model will be referred to as a model learning apparatus. Themodel learning apparatus generates a trained model by causing a neuralnetwork to perform machine learning based on training data including aplurality of training samples. As a training sample, for example, thesame data is prepared for both input and output. The model learningapparatus performs training by adding random noise with respect to inputand inputting it to an auto encoder network that is a combination of anencoder network and a decoder network. The encoder network outputs ablend dataset. The decoder network outputs original data from the blenddataset. In the encoder network, output data is set to be lower inamount than the original data. By training the auto encoder networkbased on input and output data described above, a trained model isgenerated from the encoder network. The trained model is configured insuch a manner that medical raw datasets respectively corresponding tocomponents are input and a blend dataset is output.

Another method uses a training sample that is a combination of medicalraw datasets serving as input data and respectively corresponding tocomponents, and a blend dataset serving as supervisory data andcorresponding to the medical raw datasets. The blend dataset serving assupervisory data (hereinafter referred to as a supervisory blenddataset) is explicitly given and orthogonal transform is performed onmedical raw datasets respectively corresponding to components. A trainedmodel may be generated by this method, too. The model learning apparatusperforms forward propagation by applying a neural network to medical rawdatasets respectively corresponding to components, thereby outputting ablend dataset (hereinafter referred to as an estimated blend dataset).Next, the model learning apparatus performs back propagation processingby applying a difference (error) between the estimated blend dataset andthe supervisory blend dataset to the neural network, and calculates agradient vector. Subsequently, the model learning apparatus updatesparameters of the neural network, such as a weighted matrix and a bias,etc., based on the gradient vector. A trained model is generated byrepeating the forward propagation processing, the back propagationprocessing, and the parameter update processing, while changing learningsamples.

A trained model may be generated and stored for each imaging body partof a subject. An imaging body part may be any anatomical region such asa head, a chest, an abdomen, an inferior limb, a heart, a lung, a liver,etc. A trained model for each imaging body part may be generated bytraining a neural network through machine learning based on a trainingsample relating to a single imaging body part. A trained model for eachimaging body part is stored in the memory circuitry 15 in such a mannerthat each imaging body part is correlated with information thereon.

FIG. 7 is a diagram showing a typical flow of compression processingthat processing circuitry 11 performs on medical raw datasets when atrained model for compression is generated and stored for each imagingbody part. As shown in FIG. 7, with the obtaining function 111, theprocessing circuitry 11 obtains the medical raw datasets D1 each servingas a compression target and respectively corresponding to components, ina step before the compression processing. With the obtaining function111, the processing circuitry 11 obtains information on an imaging bodypart D5 regarding a subject of the medical raw datasets D1. Informationon the imaging body part D5 is correlated with, for example, the medicalraw datasets D1.

When obtaining the medical raw datasets D1 and the information on theimaging body part D5, by implementing the compression function 112, theprocessing circuitry 11 generates a compressed dataset D3 by performingon the medical raw datasets D1 conversion processing that involves baseconversion by machine learning (hereinafter referred to as AI baseconversion).

First, with the compression function 112, the processing circuitry 11selects a trained model D6 correlated with the imaging body part D5(step SB1). In step SB1, specifically, the processing circuitry 11selects a trained model correlated with the imaging body part D5 from aplurality of trained models stored in the memory circuitry 15, and readsthe selected trained model.

In some cases, trained models that are different in terms of imagingbody part D5 are the same in terms of network configuration anddifferent in terms of parameter set. In such a case, information on theimaging body part D5 may be stored in the memory circuitry 15 in such amanner that the information is correlated with a parameter set. In stepSB1, the processing circuitry 11 selects a parameter set correlated withthe imaging body part D5 from a plurality of parameter sets stored inthe memory circuitry 15, and reads the selected parameter set. Theprocessing circuitry 11 sets the read parameter set to a neural network.In this manner, a trained model is selected.

After step SB1, the processing circuitry 11 executes AI base conversion(step SB2). Specifically, the processing circuitry 11 generates theblend dataset D2 by applying the trained model D6 selected in step SB1to the medical raw datasets D1.

After step SB2, the processing circuitry 11 generates the compresseddataset D3 by performing quantization on the blend dataset D2 using thequantization table T1 (step SB3), and then performing entropy coding onthe quantized data using the coding table T2 (step SB4), therebygenerating the compressed dataset D3. Step SB3 is the same as step SA2in FIG. 2. Step SB4 is the same as step SA3 in FIG. 2.

When AI base conversion is executed, the quantization table T1 may bechanged for each collection process of a medical raw dataset.Hereinafter, this implementation example will be described by using asan example a case in which medical raw datasets are projection datasetscollected by a dual energy scan with an X-ray computed tomographyapparatus. The following description assumes that medical raw datasetsare grouped for respective X-ray detector channels.

FIG. 8 is a diagram showing a correspondence relationship between groupsof X-ray detector channels and quantization tables. As shown in FIG. 8,for example, the X-ray detector channels are grouped into a center partDE0, intermediate parts DE1, and edge parts DE2 in a channel directionCH. Tables prepared in this case are a quantization table T10corresponding to the center part DE0, a quantization table T11corresponding to the intermediate parts DE1, and a quantization tableT12 corresponding to the edge parts DE2. A blend dataset is generatedbased on a projection dataset of the center part DE0. A blend dataset isgenerated based on a projection dataset of each intermediate part DE1. Ablend dataset is generated based on a projection dataset of each edgepart DE2. The edge parts DE2 have a tendency to detect direct X-raysthat do not pass through a subject. Projection datasets acquired in theedge parts DE2 tend to make little contribution to an image. Thus, tablevalues in the quantization table T12 are preferably set to be relativelylarge. On the other hand, the center part DE0 and the intermediate partsDE1 have a tendency to detect X-rays that passed through a subject.Projection datasets acquired in the center part DE0 and the intermediateparts DE1 tend to contribute to an image. Thus, table values in thequantization tables T10 and T11 are preferably set to be relativelysmall. In particular, since projection datasets acquired in the centerpart DE0 tend to make a large contribution to an image, table values inthe quantization table T10 are set to be smaller than those in thequantization tables T11 and T12.

The optimum compression efficiency can be achieved in accordance with aphysical position of a medical raw dataset by changing the quantizationtable T1 in accordance with the physical position of the medical rawdataset.

The above-described implementation example is premised on the trainedmodel in which medical raw datasets serving as input and a blend datasetserving as output are equal in terms of number of components. However,the present embodiment is not limited to this. Hereinafter, thisimplementation example will be described by using as an example a casein which medical raw datasets are projection datasets collected byphoton counting CT with an X-ray computed tomography apparatus.

FIG. 9 is a diagram showing a relationship between input and output of atrained model in which input and output differ in number of components.In the trained model shown in FIG. 9, output is set to be smaller innumber of components than input. Photon counting CT collects aprojection dataset for each energy bin; however, projection datasets forall energy bins are not always necessary. For example, assume that threeenergy bins are prepared. The first energy bin is for discriminating anX-ray photon that passed through water. The second energy bin is fordiscriminating an X-ray photon that passed through a bone. The thirdenergy bin is for discriminating noise. A projection dataset for thethird energy bin for discriminating noise may not be compressed becauseit is not necessary for imaging. In the first to the third energy bins,projection datasets for the first and second energy bins are convertedinto a blend dataset, whereas a projection dataset for the third energybin is not converted into the blend dataset. This further increasescompression efficiency.

A trained model shown in FIG. 9 corresponds to a neural network that istrained in such a manner that the first, second, and third medical rawdatasets respectively corresponding to the first, second, and thirdcomponents are input and a blend dataset corresponding to the first andsecond medical raw datasets is output. A training sample is acombination of the first, second, and third medical raw datasets eachserving as input data and a blend dataset serving as supervisory dataand corresponding to the first and second medical raw datasets. Theblend dataset that is supervisory data is generated by performingorthogonal transform on the first and second medical raw datasets.

Next, the decoding processing with the decoding function 113 of theprocessing circuitry 11 will be described.

FIG. 10 is a diagram showing a typical flow of decoding processing thatthe processing circuitry shown in FIG. 1 performs on a compresseddataset. In a step before the decoding processing, with the obtainingfunction ill, the processing circuitry 11 obtains the compressed datasetD3 that is a compression target.

Upon acquisition of the compressed dataset D3, by implementing thedecoding function 113, the processing circuitry 11 performs the decodingprocessing on the compressed dataset D3, thereby decoding it to aplurality of medical raw datasets D8 respectively corresponding to aplurality of components. The decoding processing is performed byretrograding a conversion process (compression process) from theplurality of medical raw datasets D1 respectively corresponding to theplurality of components to the compressed dataset D3. Hereinafter, amedical raw dataset obtained by decoding will be referred to as adecoded medical raw dataset.

Specifically, the processing circuitry 11 performs entropy decoding onthe compressed dataset D3 using the coding table T3 (step SC1). In stepSC1, the processing circuitry 11 performs entropy decoding by referringto attendant information regarding a compression method, which isassociated with the compressed dataset D3. A quantization progression isgenerated by entropy decoding.

After SC1, the processing circuitry 11 performs inverse quantization onthe quantization progression using a quantization table T4 (step SC2).In step SC2, the processing circuitry 11 performs inverse quantizationby referring to attendant information regarding a compression method,which is associated with the compressed dataset D3. By the inversequantization, a blend dataset D7 is generated.

After step SC2, the processing circuitry 11 performs inverse baseconversion on the blend dataset D7, thereby generating a decoded medicalraw dataset D8 (step SC3). For example, the processing circuitry 11decodes the blend dataset D7 to the decoded medical raw datasets D8corresponding to all the components taken in the compressed dataset D3.Furthermore, the processing circuitry 11 may decode the blend dataset D7to the decoded medical raw dataset D8 corresponding to only componentsfreely selected from all the components incorporated into the compresseddataset D3. For example, the processing circuitry 11 may decode theblend dataset D7 to the decoded medical raw dataset D8 corresponding toonly a component serving as an imaging target and selected by a user byway of the input interface 14.

The inverse base conversion is inverse conversion of base conversionfrom the medical raw datasets D1 respectively corresponding tocomponents to the blend dataset D2. For example, if orthogonal transformis performed as base conversion to the blend dataset D2, inverseorthogonal conversion is performed as inverse base conversion. Morespecifically, if discrete cosine transform is performed as orthogonaltransform, inverse discrete cosine transform is performed as inverseorthogonal transform.

Inverse conversion coefficient f (j, k, l) obtained by three-dimensionalinverse discrete cosine transform is expressed by the followingexpression. Herein, F (u, v, w) is a value of a blend dataset in point(u, v, w) in a spatial frequency space. N represents a size of block. Inthe case of four-dimensional spatial frequency space, the followingexpression may be four-dimensionally expanded.

${f\left( {j,k,l} \right)} = {\frac{8{C(u)}{C(\nu)}{C(w)}}{N^{3}}{\sum\limits_{j = 0}^{N - 1}{\sum\limits_{k = 0}^{N - 1}{\sum\limits_{l = 0}^{N - 1}{{F\left( {u,v,w} \right)}\cos \frac{\left( {{2j} + 1} \right)u\pi}{2N}\cos \; \frac{\left( {{2k} + 1} \right)v\pi}{2N}\; \cos \frac{\left( {{2l} + 1} \right)w\pi}{2N}}}}}}$

Inverse discrete cosine transform may be processed independently foreach dimension, as in discrete cosine transform. For example, in thecase of a three-dimensional spatial frequency space, one-dimensionalinverse discrete cosine transform is applied to the blend dataset D7 inthe order of fx-dimension, fy-dimension, and fz-dimension. This order ofgx-dimension, gy-dimension, and gz-dimension is not a limitation. Theinverse discrete cosine transform may be performed in any order, forexample, the order of fz-dimension, fy-dimension, and fx-dimension. Thesame applies to the case of a four-dimensional spatial frequency space.Inverse conversion coefficient f (j) obtained by one-dimensional inversediscrete cosine transform can be expressed by the following expression.

${f(j)} = {\frac{2{C(u)}}{N}{\sum\limits_{j = 0}^{N - 1}{{F(u)}\cos \; \frac{\left( {{2j} + 1} \right)u\; \pi}{2N}}}}$

In this manner, the decoding processing that the processing circuitry 11performs on a compressed dataset is completed. Thereafter, medical imagedata is generated based on the decoded medical raw dataset, is subjectedto appropriate image processing, and is displayed on the display 13.

According to the above decoding processing, a compressed dataset that isa one-dimensional code sequence generated by the compression processingdescribed above can be decoded by utilizing a conversion process in thecompressed processing to a plurality of decoded medical raw datasetsthat are defined by two or more dimensional domain representation andrespectively correspond to a plurality of components. The compresseddataset is data compressed with a high efficiency in terms of bothspatial dimension and a component dimension. Thus, the compressed datacan be decoded with a high efficiency in terms of both a spatialdimension and a component dimension through the decoding processingutilizing a conversion process in the compression processing describedabove.

The decoding processing described above may adopt any method by which adecoded medical raw dataset can be generated from a compressed dataset.Various modifications can be made to the decoding processing.Hereinafter, a modification of decoding processing will be described.

Inverse base conversion SC3 in the above decoding processing may beperformed through machine learning using a trained model.

FIG. 11 is a schematic diagram showing a relationship between input andoutput of a trained model for decoding processing. As shown in FIG. 11,a trained model corresponds to a neural network that is trained in sucha manner that a blend dataset is input and medical raw datasetsrespectively corresponding to components corresponding to the compresseddataset is output.

A trained model is generated by a model learning apparatus. The modellearning apparatus generates a trained model by causing a neural networkto perform machine learning based on training data including a pluralityof training samples. As a training sample, for example, the same data isprepared for both input and output. The model learning apparatusperforms learning by adding random noise with respect to input andinputting it to an auto encoder network that is a combination of anencoder network and a decoder network. The encoder network outputs ablend dataset. The decoder network outputs original data from the blenddataset. In the encoder network, output data is set to be lower inamount than original data. By training the auto encoder network based oninput and output data described above, a trained model is generated fromthe encoder network. The trained model is configured in such a mannerthat a blend dataset is input and decoded medical raw datasetsrespectively corresponding to components are output.

Another method uses a training sample that is a combination of a blenddataset serving as input data and decoded medical raw datasetsrespectively corresponding to components corresponding to the blenddataset serving as supervisory data. The decoded medical raw datasetsserving a supervisory data (hereinafter referred to as supervisorydecoded medical raw datasets) are explicitly given and inverseorthogonal transform is performed on the blend dataset. A trained modelmay be generated by this method, too. The model learning apparatusperforms forward propagation by applying a neural network to a blenddataset, thereby outputting medical raw datasets respectivelycorresponding to components (hereinafter referred to as estimateddecoded medical raw datasets). Next, the model learning apparatusperforms back propagation processing by applying a difference (error)between the estimated decoded medical raw dataset and the supervisorydecoded medical raw dataset to the neural network, and calculates agradient vector. Subsequently, the model learning apparatus updatesparameters of the neural network based on the gradient vector. A trainedmodel is generated by repeating the forward propagation processing, theback propagation processing, and the parameter update processing, whilechanging learning samples.

As in a trained model for compression, a trained model for decoding maybe generated and stored for each imaging body part of a subject. Atrained model for decoding for each imaging body part may be generatedby training a neural network through machine learning based on atraining sample regarding a single imaging body part. A trained modelfor compression for each imaging body part is stored in the memorycircuitry 15 in such a manner that each imaging body part is correlatedwith information thereon.

FIG. 12 is a diagram showing a typical flow of decoding processing thatthe processing circuitry 11 performs on a compressed dataset when atrained model for decoding is generated and stored for each imaging bodypart. As shown in FIG. 12, in a step before the decoding processing,with the obtaining function 111, the processing circuitry 11 obtains acompressed dataset D3 that is a compression target. With the obtainingfunction 111, the processing circuitry 11 obtains information on animaging body part D5 regarding a subject of a compressed dataset.

When obtaining the compressed dataset D3 and the information on theimaging body part D5, by implementing the decoding function 113, theprocessing circuitry 11 generates a plurality of decoded medical rawdatasets D8 by performing on the compressed dataset D3 inverseconversion processing that involves inverse base conversion by machinelearning (hereinafter referred to as AI inverse base conversion).

First, the processing circuitry 11 performs entropy decoding on thecompressed dataset D3 using the coding table T3 (step SD1) and performsinverse quantization on a quantization progression using a quantizationtable T4 (step SD2), thereby generating the blend dataset D7. Step SD1is the same as step SC1 in FIG. 10. Step SD2 is the same as step SC2 inFIG. 10.

On the other hand, with the decoding function 113, the processingcircuitry 11 selects a trained model D9 for decompression correlatedwith the imaging body part D5 (step SD3). In step SD3, the processingcircuitry 11 selects a trained model for decoding correlated with theimaging body part D5 from a plurality of trained models for decodingstored in the memory circuitry 15, and reads the selected trained modelfor decoding.

In some cases, trained models that are different in terms of imagingbody part D5 are the same in terms of network configuration anddifferent in terms of parameter set. In such a case, information on theimaging body part D5 may be stored in the memory circuitry 15 in such amanner that the information is correlated with a parameter set. In stepSD3, the processing circuitry 11 selects a parameter set correlated withthe imaging body part D5 from a plurality of parameter sets stored inthe memory circuitry 15, and reads the selected parameter set. Theprocessing circuitry 11 sets the read parameter set to a neural networkfor decoding. In this manner, a trained model for decoding is selected.

After steps SD2 and SD3, the processing circuitry 11 executes AI inversebase conversion (step SD4). Specifically, the processing circuitry 11generates a plurality of medical raw datasets D8 respectivelycorresponding to a plurality of components by applying a trained modelD9 for decoding selected in step SD3 to the blend dataset D7.

In this manner, the decoding processing utilizing AI inverse baseconversion is completed. Step SD3 may be performed any time before SD4is performed. That is, step SD3 may be performed prior to step SD1 orstep SD2.

The above-described implementation example is premised on the trainedmodel in which a blend dataset serving as input and decoded medical rawdatasets serving as output are equal in terms of number of components.However, the present embodiment is not limited to this. Hereinafter,this implementation example will be described by using as an example acase in which medical raw datasets are projection datasets collected byphoton counting CT with an X-ray computed tomography apparatus.

FIG. 13 is a diagram showing a relationship between input and output ofa trained model for decoding that differs in number of componentsbetween input and output. In the trained model shown in FIG. 13, outputis set to be smaller in number of components than input. Photon countingCT collects a projection dataset for each energy bin; however,projection datasets for all energy bins are not always necessary. Forexample, assume that three energy bins are prepared. The first energybin is for discriminating an X-ray photon that passed through water. Thesecond energy bin is for discriminating an X-ray photon that passedthrough a bone. The third energy bin is for discriminating noise. Aprojection dataset for the third energy bin for discriminating noise maynot be decoded because it is not necessary for imaging. In the first tothe third energy bins, projection datasets of the first and secondenergy bins are decoded, whereas a projection dataset of the thirdenergy bin is not decoded. This further increases compressionefficiency.

A trained model shown in FIG. 13 corresponds to a neural network that istrained in such a manner that a blend dataset corresponding to thefirst, second, and third components is input and the first and secondmedical raw datasets respectively corresponding to the first and secondcomponents are output. A training sample is a combination of the blenddataset serving as input data and relating to the first, second, andthird medical raw datasets and the first and second medical raw datasetsserving as supervisory data. The first and second medical raw datasetsthat are supervisory data may be generated and extracted by performingorthogonal transform on the first, second, and third blend datasets, ormay be the original first and second medical raw datasets.

The above example was described based on the premise that the number ofcomponents on the output side is smaller by one than the number ofcomponents on the input side. However, the present embodiment is notlimited to this. For example, the number of components on the outputside may be smaller by two than the number of components on the inputside, or may be one regardless of the number of components on the inputside.

As described above, the medical data processing apparatus 1 includes theprocessing circuitry 11. The processing circuitry 11 implements at leastthe obtaining function 111 and the decoding function 113. Byimplementing the obtaining function 111, the processing circuitry 11obtains a compressed dataset obtained by compressing a plurality offirst medical datasets defined by the first domain representationincluding a spatial domain dimension and a component dimension andrespectively corresponding to a plurality of components, by way of anintermediate dataset defined by the second domain representationincluding a frequency dimension. By implementing the decoding function113, the processing circuitry 11 decodes the compressed dataset to thesecond medical dataset defined by the first domain representation basedon a conversion process from the plurality of first medical datasets tothe compressed dataset.

The above structure utilizes not only a spatial correlation but also acomponent correlation, and thus enables a plurality of medical datasetsrespectively corresponding to a plurality of components to be compressedwith a high efficiency by reducing both redundancy of a spatial domainand redundancy between components. Accordingly, the medical datasets canbe compressed with a high efficiency. In addition, the medical datasetscan be decoded from the compressed dataset with a high efficiency basedon the compression process described above.

(First Modification)

The medical data processing apparatus 1 shown in FIG. 1 is configured insuch a manner that the processing circuitry 11 having both thecompression function 112 and the decoding function 113 is mounted on themedical data processing apparatus 1. However, the compression function112 and the decoding function 113 may be dispersed and mounted onseparate hardware devices. Hereinafter, a first modification will bedescribed. In the description below, structural elements havingsubstantially the same functions as those in the present embodiment willbe denoted by the same reference symbols, and repeat descriptions ofsuch elements will be given only where necessary.

FIG. 14 is a diagram showing a configuration of a medical imagediagnostic apparatus according to a first modification. As shown in FIG.14, the medical image diagnostic apparatus according to the firstmodification includes a medical imaging mechanism 16 and a medical dataprocessing apparatus 2 that are connected to each other via a cable or anetwork. The medical imaging mechanism 16 corresponds to an imagingmechanism for the medical image diagnostic apparatus described above.The medical data processing apparatus 2 corresponds to a consoleconfigured to control the medical imaging mechanism 16, perform imagereconstruction, and so on.

The medical imaging mechanism 16 collects a plurality of medical rawdatasets respectively corresponding to a plurality of components byperforming medical imaging on a subject in accordance with each imagingprinciple. Processing circuitry of the medical imaging mechanism 16 hasthe compression function 112. With the compression function 112, themedical imaging mechanism 16 generates a compressed dataset based on theplurality of collected medical raw datasets. The medical imagingmechanism 16 transmits the compressed dataset to the medical dataprocessing apparatus 2.

The medical data processing apparatus 2 includes the processingcircuitry 11, the communication interface 12, the display 13, the inputinterface 14, and the memory circuitry 15. The processing circuitry 11includes a processor such as a CPU or GPU. By activating variousprograms installed in the memory circuitry 15, etc., the processorimplements the obtaining function 111, the decoding function 113, theimage generation function 114, the display control function 115, etc.

By implementing the obtaining function ill, the processing circuitry 11obtains a compressed dataset transmitted from the medical imagingmechanism 16. The processing circuitry 11 may obtain a compresseddataset directly from the medical imaging mechanism 16, by way of thecommunication interface 12, a portable storage medium, etc., or mayobtain a compressed dataset transmitted by the medical imaging mechanism16 and stored in the memory circuitry 15.

By implementing the decoding function 113, the processing circuitry 11decodes the compressed dataset to a decoded medical raw dataset definedby the first domain representation based on a conversion process fromthe plurality of medical raw datasets to the compressed dataset.

The image generation function 114, the display control function 115, thecommunication interface 12, the display 13, the input interface 14, andthe memory circuitry 15 are the same as those in the embodimentdescribed above. Thus, the description for them is omitted herein.

Hereinafter, a specific example according to the first modification willbe described.

When the medical imaging mechanism 16 corresponds to a gantry of anX-ray computed tomography apparatus, the processing circuitry thatimplements the compression function 112 is provided in a rotating unit.In the rotating unit, a plurality of projection datasets respectivelycorresponding to a plurality of components are converted into acompressed dataset. In the case of dual energy scan, a plurality ofprojection datasets respectively corresponding to a plurality of tubevoltage values are converted into a compressed dataset. A compresseddataset is transferred to the medical data processing apparatus 2 by wayof a fixed unit. With the decoding function 113, the compressed datasetis decoded to a plurality of decoded projection datasets respectivelycorresponding to a plurality of tube voltage values. With the imagegeneration function 114, a monochromatic X-ray image, a materialdecomposition image, or the like is generated based on the plurality ofdecoded projection datasets and is displayed on the display 13.

In the case of photon counting CT, a plurality of projection datasetsrespectively corresponding to a plurality of energy bins are convertedinto a compressed dataset. A compressed dataset is transferred to themedical data processing apparatus 2 by way of a fixed unit. With thedecoding function 113, the compressed dataset is decoded to a pluralityof decoded projection datasets respectively corresponding to a pluralityof energy bins. With the image generation function 114, a monochromaticX-ray image, a material decomposition image, or the like is generatedbased on the plurality of decoded projection datasets and is displayedon the display 13.

When the medical imaging mechanism 16 corresponds to a gantry of amagnetic resonance apparatus, the processing circuitry that implementsthe compression function 112 is provided in a reception circuitry. Inthe reception circuitry, a plurality of k-space datasets respectivelycorresponding to a plurality of receiver channels are converted into acompressed dataset. A compressed dataset is transferred to the medicaldata processing apparatus 2 by way of a fixed unit. With the decodingfunction 113, the compressed dataset is decoded to a plurality ofdecoded k-space datasets respectively corresponding to a plurality oftube voltage values. With the image generation function 114, an MR imageis generated based on the plurality of decoded k-space datasets and isdisplayed on the display 13.

As described above, according to the first modification, the medicalimaging mechanism 16 transmits compressed data and thus realizes areduced amount of transmitted data as compared to the case oftransmitting a plurality of medical raw datasets respectivelycorresponding to a plurality of components. This enables high-speed datatransmission and a simplified transmission facility. The decodingfunction 113 mounted on the medical data processing apparatus 2 enablesthe medical data processing apparatus 2 to, e.g., generate and image amedical image based on a compressed dataset.

(Second Modification)

In some cases, a plurality of medical raw datasets respectivelycorresponding to a plurality of components are used as a trainingsample. Machine learning requires many training samples, and thusrequires a large storage area to store training samples. In the medicaldata processing system according to a second modification, thecompression function 112 is mounted on each medical image diagnosticapparatus that generates a training sample, and the decoding function113 is mounted on the model learning apparatus. Hereinafter, the medicaldata processing system according to the second modification will bedescribed. In the description below, structural elements havingsubstantially the same functions as those in the present embodiment willbe denoted by the same reference symbols, and a repeat description ofsuch elements will be given only where necessary.

FIG. 15 is a diagram showing a configuration of a medical dataprocessing system according to the second modification. As shown in FIG.15, the medical data processing system according to the secondmodification includes a plurality of medical image diagnosticapparatuses 4A, 4B, and 4C, a database 5, and a model learning apparatus6. FIG. 15 shows three medical image diagnostic apparatuses, i.e., themedical image diagnostic apparatuses 4A, 4B, and 4C. However, the numberof medical image diagnostic apparatuses 4 provided in the medical dataprocessing system is not particularly limited, and may be two or less orfour or more. Hereinafter, the medical image diagnostic apparatuses 4A,4B, and 4C will be simply and collectively referred to as the medicalimage diagnostic apparatus 4 without distinction between the apparatuses4A, 4B, and 4C.

The medical image diagnostic apparatus 4 collects a plurality ofdatasets respectively corresponding to a plurality of components. Themedical image diagnostic apparatuses may be the same or different interms of type. By implementing the compression function 112, the medicalimage diagnostic apparatus 4 generates a compressed dataset byperforming compression processing on a plurality of medical rawdatasets. The generated compressed dataset and a plurality of medicalraw datasets based on the compressed dataset are transmitted as atraining sample to the database 5. A plurality of medical raw datasetscontained in a training sample may be subjected to lossless compression.

The database 5 corresponds to a mass-storage device or a computerprovided with the mass-storage device, which stores a training sampleincluding a compressed dataset and medical raw datasets based on thecompressed dataset, which are transmitted from the medical imagediagnostic apparatus 4.

As described above, the model learning apparatus 6 corresponds to acomputer that generates a trained model by causing a neural network toperform machine learning based on training data including a plurality oftraining samples. By implementing the decoding function 113, the modellearning apparatus 6 according to the second modification decodes datato a plurality of decoded medical raw datasets respectivelycorresponding to a plurality of components based on a compressed datasetcontained in a single training sample. The plurality of decoded medicalraw datasets are utilized as supervisory data. The model learningapparatus 6 performs learning of parameters of a neural network based onthe compressed dataset and the decoded medical raw datasets. The modellearning apparatus 6 repeats parameter learning while changing acompressed dataset, thereby generating a trained model.

The configuration of the medical data processing system according to thesecond modification is not limited to the one shown in FIG. 14. Forexample, the database 5 may be incorporated into the model learningapparatus 6. The above description is based on the premise that thedecoding function 113 is mounted on the model learning apparatus 6.However, the decoding function 113 may be mounted on a computerseparated from the model learning apparatus 6. In the above description,the compression function 112 is mounted on the medical image diagnosticapparatus 4. However, the compression function 112 may be mounted on acomputer different from the medical image diagnostic apparatus 4.

As described above, by providing the medical data processing systemaccording to the second modification with the compression function 112,training samples can be compressed and stored in a case of using medicalraw datasets respectively corresponding to components as trainingsamples for machine learning. This enables a reduced storage area fortraining samples and a simplified storage facility. By providing themedical data processing system according to the second modification withthe decoding function 113, machine learning can be performedappropriately by decoding compressed training samples.

(Third Modification)

The processing circuitry 11 according to a third modification mayperform prediction between a plurality of components. When medicaldatasets are collected by an X-ray computed tomography apparatus, theprocessing circuitry 11 predicts projection datasets in units of apredetermined number of rows of an X-ray detector. In the detector,projection data is similar between adjacent rows. Thus, the predictionefficiency is high. When medical raw datasets are acquired by a magneticresonance imaging apparatus, the processing circuitry 11 predictsk-space datasets in units of blocks equal to the integral multiple ofthe number of times that a read-out gradient magnetic field is applied,in other words, the number of echo trains, for each block of pulsesequences with blocks such as FFE or FSE. K-space data is similarbetween adjacent blocks. Thus, the prediction efficiency is high. Theprediction may be performed by a method prescribed by H.264, etc., suchas Intra prediction, DC prediction, etc. The prediction may be performedby a recurrent neural network (RNN). By performing the prediction, theamount of data can be further reduced. If the prediction is performed ina compression process, a similar prediction to the one in thecompression process may be performed in a decoding process, too. Thatis, the processing circuitry 11 may predict decoded projection datasetsin units of a predetermined number of rows in an X-ray detector orpredict decoded k-space datasets in units of blocks equal to an integralmultiple of a number of echo trains.

(Fourth Modification)

The above embodiment was described based on the premise that a pluralityof medical raw data sets respectively corresponding to a plurality ofcomponents are subjected to base conversion. However, the presentembodiment is not limited to this. The processing circuitry 11 may inputa residual error between a plurality of medical raw datasetsrespectively corresponding to a plurality of components into a trainedmodel, and output a blend dataset. Specifically, the processingcircuitry 11 inputs a residual error in a medical raw dataset betweentwo components that are physically adjacent to each other, into atrained model. Alternatively, the processing circuitry 11 may generate aplurality of ACT coefficient datasets by performing orthogonal transformon a plurality of medical raw datasets respectively corresponding to aplurality of components, input a residual error between the plurality ofACT coefficient datasets, and output a blend dataset. By inputting of aresidual error into a trained model in this manner, the number of layersfor the trained model can be decreased.

(Fifth Modification)

The above embodiment was described based on the premise that a pluralityof medical raw datasets respectively corresponding to a plurality ofcomponents are compressed. However, the present embodiment is notlimited to this. Data to be compressed may be any data includingdatasets respectively corresponding to components. For example, medicalimage datasets respectively corresponding to components may becompressed. For example, data to be compressed may be a plurality of CTimage datasets respectively corresponding to a plurality of tubevoltages, a plurality of CT image datasets respectively corresponding toa plurality of energy bins, or a plurality of MR image datasetsrespectively corresponding to a plurality of receiver channels.Similarly, a compressed dataset obtained by compressing a plurality ofmedical image datasets respectively corresponding to a plurality ofcomponents may be decoded. In this manner, data is decoded from acompressed dataset. Examples of compressed data include a plurality ofCT image datasets respectively corresponding to a plurality of tubevoltages, a plurality of CT image datasets respectively corresponding toa plurality of energy bins, and a plurality of MR image datasetsrespectively corresponding to a plurality of receiver channels.

According to at least one of the embodiments described above, theefficiency of compression and/or decoding with respect to multicomponentmedical data can be improved.

The term “processor” used in the above explanation indicates, forexample, a circuit, such as a CPU, a GPU, or an Application SpecificIntegrated Circuit (ASIC), and a programmable logic device (for example,a Simple Programmable Logic Device (SPLD), a Complex Programmable LogicDevice (CPLD), and a Field Programmable Gate Array (FPGA)). A processorrealizes its functions by reading and executing a program stored inmemory circuitry. Instead of storing a program on memory circuitry, aprogram may be directly integrated into circuitry of a processor. Inthis case, a processor reads and executes a program integrated intocircuitry, thereby realizing its functions. The function correspondingto the program may be realized by a combination of logic circuits, notby executing the program. Each processor of the present embodiment isnot limited to a configuration as a single circuit; a plurality ofindependent circuits may be combined into one processor to realize thefunction of the processor. Furthermore, a plurality of constituentelements shown in FIGS. 1, 14, and 15 may be integrated into oneprocessor to realize their functions.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

1. A medical data processing apparatus comprising: processing circuitryconfigured to: obtain a compressed channel of data generated bycompressing a plurality of first medical channels of data defined byfirst domain representation and respectively corresponding to aplurality of components, via an intermediate channel of data defined bysecond domain representation; and decode the compressed channel of datato a second medical channel of data defined by the first domainrepresentation based on a conversion process from the plurality of firstmedical channels of data to the compressed channel of data.
 2. Themedical data processing apparatus according to claim 1 wherein: thefirst domain representation includes a dimension of a spatial domain anda dimension of a component domain; and the second domain representationincludes a dimension of a spatial frequency domain.
 3. The medical dataprocessing apparatus according to claim 1, wherein the processingcircuitry decodes the compressed channel of data to the second medicalchannel of data with respect to all of the components or a selectedcomponent of the components.
 4. The medical data processing apparatusaccording to claim 1, wherein the processing circuitry generates adecoded intermediate channel of data defined by the second domainrepresentation by performing entropy decoding and inverse quantizationon the compressed channel of data, and generates the second medicalchannel of data by performing inverse base conversion on the decodedintermediate channel of data.
 5. The medical data processing apparatusaccording to claim 4, wherein the processing circuitry executes inverseorthogonal conversion as the inverse base conversion.
 6. The medicaldata processing apparatus according to claim 4, wherein the processingcircuitry uses as the inverse base conversion a trained model trained insuch a manner that an intermediate channel of data is input and a secondmedical channel of data is output.
 7. The medical data processingapparatus according to claim 6, further comprising: a storage deviceconfigured to store a plurality of trained models in such a manner thatthe trained models are correlated with a plurality of imaging bodyparts, wherein the processing circuitry selects from the plurality oftrained models a trained model correlated with an imaging body partrelating to the compressed channel of data.
 8. The medical dataprocessing apparatus according to claim 6, wherein the processingcircuitry performs prediction in units of a number of rows in an X-raydetector or in units of blocks equal to an integral multiple of a numberof echo trains.
 9. The medical data processing apparatus according toclaim 1, wherein the plurality of first medical channels of data aregenerated in units of a number of rows in an X-ray detector or in unitsof blocks equal to an integral multiple of a number of echo trains. 10.The medical data processing apparatus according to claim 1, wherein theplurality of components correspond to a plurality of tube voltages in adual energy scan, a plurality of energy bins in photon counting CT, anda plurality of receiver channels in magnetic resonance imaging.
 11. Themedical data processing apparatus according to claim 1, wherein thecompressed channel of data contains information on all or part of theplurality of components.
 12. The medical data processing apparatusaccording to claim 1, wherein the processing circuitry generates thecompressed channel of data from the plurality of first medical channelsof data.
 13. A medical data processing method comprising: obtainingcompressed data acquired by compressing a plurality of first medicalchannels of data defined by first domain representation and respectivelycorresponding to a plurality of components, via an intermediate channelof data defined by second domain representation; and decoding thecompressed channel of data to the second medical channel of data definedby the first domain representation based on a conversion process fromthe plurality of first medical channels of data to the compressedchannel of data.
 14. A medical image diagnostic apparatus comprising: agantry configured to acquire a plurality of first medical channels ofdata defined by first domain representation and respectivelycorresponding to a plurality of components; and processing circuitryconfigured to generate compressed data by compressing the plurality offirst medical channels of data via an intermediate channel of datadefined by second domain representation.
 15. The medical imagediagnostic apparatus according to claim 14, further comprising adisplay, wherein: the processing circuitry decodes the compressed datato a second medical dataset defined by the first domain representationbased on a conversion process from the plurality of first medicalchannels of data to the compressed data, and reconstructs a medicalimage based on the second medical channel of data; and the displaydisplays the medical image.